Computational Learning Theory

Instructor: Adam Klivans

Course meets Monday and Wednesday at 11:00AM in RLM 6.120

Office Hours: Monday 1:30-3:00pm in TAY 3.148

Course Description

Provably efficient algorithms are essential for solving nontrivial
machine learning problems. The primary goal of computational learning
theory is the development of a rich set of algorithmic tools for
efficiently solving common classification tasks. In this course, we
survey well-established models of learning and describe a variety of
algorithms for learning concept classes within these models. Topics
include PAC learning, online learning, halfspace-based learning,
Fourier-based learning, boosting, learning with noise, random
projection, and query learning.
In addition, we describe how to apply results from cryptography and
computational complexity to prove the intractability of various
machine learning problems. No background is required for this course
other than a working knowledge of basic probability and mathematical
maturity. Coursework includes problem sets (2/5 of final grade),
scribe notes (1/5 of final grade), and a final project (the final
project may be of a more 'applied' nature; 2/5 of final grade).

This course is a selection of the best lectures from the two-semester
course on computational learning theory I taught in 2005.